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<a href="#pub-methods">Public Member Functions</a> &#124;
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<p><code>#include &lt;<a class="el" href="rtrl_8h_source.html">rtrl.h</a>&gt;</code></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:aac03b72729c67ddb25db089171419cee"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#aac03b72729c67ddb25db089171419cee">RTRL</a> (int num_Sensors, int num_Motors)</td></tr>
<tr class="separator:aac03b72729c67ddb25db089171419cee"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a43fc82af4df69df76841433d454488ac"><td class="memItemLeft" align="right" valign="top">virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#a43fc82af4df69df76841433d454488ac">~RTRL</a> ()</td></tr>
<tr class="separator:a43fc82af4df69df76841433d454488ac"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac20cfd576eadeb8b1047a52c4ee6836a"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#ac20cfd576eadeb8b1047a52c4ee6836a">setDesiredOut</a> (Matrix fut_sensor)</td></tr>
<tr class="separator:ac20cfd576eadeb8b1047a52c4ee6836a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a8f6ce1e926cd66a14f86faab2a793096"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#a8f6ce1e926cd66a14f86faab2a793096">setState</a> (Matrix motors)</td></tr>
<tr class="separator:a8f6ce1e926cd66a14f86faab2a793096"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a979080415f69760e4b9bcafb4bae4852"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#a979080415f69760e4b9bcafb4bae4852">predict</a> ()</td></tr>
<tr class="separator:a979080415f69760e4b9bcafb4bae4852"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aaf22833404251940557902695043d86d"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#aaf22833404251940557902695043d86d">getModelMatrix</a> (Matrix input)</td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
<tr class="memitem:aef2f207bf3fbd5c021c38ac47c43df1b"><td class="memItemLeft" align="right" valign="top">double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#aef2f207bf3fbd5c021c38ac47c43df1b">learningRate</a></td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pri-methods"></a>
Private Member Functions</h2></td></tr>
<tr class="memitem:ae5fe58a009095291e26cc03fbf38fd8c"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#ae5fe58a009095291e26cc03fbf38fd8c">simulateNet</a> ()</td></tr>
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<tr class="memitem:a08b0ef1c329727b79240ee738abd2200"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#a08b0ef1c329727b79240ee738abd2200">getNodeSensitivity</a> (Matrix updatedNodes, Matrix nodesValue)</td></tr>
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<tr class="memitem:a335e29295c02ad387560296392bc2c72"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#a335e29295c02ad387560296392bc2c72">updateWeights</a> (Matrix updatedNodes)</td></tr>
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<tr class="memitem:ac065c297b0a6bb668504798903aaa1e0"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#ac065c297b0a6bb668504798903aaa1e0">kronneckerDelta</a> (int i, int k)</td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pri-static-methods"></a>
Static Private Member Functions</h2></td></tr>
<tr class="memitem:a76632a4b39e49c7e2b7383f4b205531b"><td class="memItemLeft" align="right" valign="top">static double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#a76632a4b39e49c7e2b7383f4b205531b">squash</a> (double node)</td></tr>
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<tr class="memitem:a3f6c5fb7336b2d9a5fa32c5c7fd8a098"><td class="memItemLeft" align="right" valign="top">static double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#a3f6c5fb7336b2d9a5fa32c5c7fd8a098">squash_derivative</a> (double node)</td></tr>
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Private Attributes</h2></td></tr>
<tr class="memitem:ab8432648bd198b508d3df70795a2e530"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#ab8432648bd198b508d3df70795a2e530">nodeState</a></td></tr>
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<tr class="memitem:aacf8aed7c8db184aac677b92cfb897c0"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#aacf8aed7c8db184aac677b92cfb897c0">numInputNodes</a></td></tr>
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<tr class="memitem:a7efbd35c2dc8ce128d90beca0a48cfcb"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#a7efbd35c2dc8ce128d90beca0a48cfcb">numReservoirNodes</a></td></tr>
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<tr class="memitem:a8812c0b19a5dcf8bd60358eb2943ca5a"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#a8812c0b19a5dcf8bd60358eb2943ca5a">numOutputNodes</a></td></tr>
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<tr class="memitem:a008e08ccdfed2dc91d136f43def827f1"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#a008e08ccdfed2dc91d136f43def827f1">outputState</a></td></tr>
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<tr class="memitem:ada2be9a65806afca86c8b74ec4043800"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#ada2be9a65806afca86c8b74ec4043800">weights</a></td></tr>
<tr class="separator:ada2be9a65806afca86c8b74ec4043800"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a37638ae70d7bbd0b03aa5c1b3ce55d47"><td class="memItemLeft" align="right" valign="top">vector&lt; Matrix &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#a37638ae70d7bbd0b03aa5c1b3ce55d47">nodeSensitivity</a></td></tr>
<tr class="separator:a37638ae70d7bbd0b03aa5c1b3ce55d47"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7c5691d2b26a3297c7b5502c1e5cbba3"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRTRL.html#a7c5691d2b26a3297c7b5502c1e5cbba3">desiredSens</a></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>File: <a class="el" href="rtrl_8h.html">rtrl.h</a></p>
<p>Class that implements the Real Time Recurrent Learning (<a class="el" href="classRTRL.html">RTRL</a>) rule in Recurrent Neural Networks as presented in section 3.1.1 of the thesis.</p>
<p><a class="el" href="classRTRL.html">RTRL</a> is a gradient descent based algorithm that recurrently calculates the sensitivity of output nodes on changes of weights that occur in the RNN</p>
<dl class="section author"><dt>Author</dt><dd>: Athanasios Polydoros </dd></dl>
<dl class="section version"><dt>Version</dt><dd>: 1.0 Created on 25 June 2013, 19:36 </dd></dl>
</div><h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
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          <td class="memname">RTRL::RTRL </td>
          <td>(</td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>num_Sensors</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>num_Motors</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
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<p>Class constructor that initialise the members of class. Has to be called in the controller's init method.</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">num_Sensors</td><td>The number of robot sensors, used as network's outputs </td></tr>
    <tr><td class="paramname">num_Motors</td><td>The number of motors, used as inputs </td></tr>
  </table>
  </dd>
</dl>

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          <td class="memname">RTRL::~RTRL </td>
          <td>(</td>
          <td class="paramname">)</td><td></td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">virtual</span></span>  </td>
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<p>Class destructor </p>

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<h2 class="groupheader">Member Function Documentation</h2>
<a class="anchor" id="aaf22833404251940557902695043d86d"></a>
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          <td class="memname">Matrix RTRL::getModelMatrix </td>
          <td>(</td>
          <td class="paramtype">Matrix&#160;</td>
          <td class="paramname"><em>input</em>)</td><td></td>
          <td></td>
        </tr>
      </table>
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<p>Calculate the jacobian matrix of the network. The derivative of outputs wrt inputs. Should be called after activation of network</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">the</td><td>input nodes values </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>MxM Jacobian matrix </dd></dl>

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<a class="anchor" id="a08b0ef1c329727b79240ee738abd2200"></a>
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          <td class="memname">void RTRL::getNodeSensitivity </td>
          <td>(</td>
          <td class="paramtype">Matrix&#160;</td>
          <td class="paramname"><em>updatedNodes</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">Matrix&#160;</td>
          <td class="paramname"><em>nodesValue</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">private</span></span>  </td>
  </tr>
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<p>Calculate the sensitivity of output nodes.</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">updatedNodes</td><td>Matrix of the activated nodes values </td></tr>
    <tr><td class="paramname">nodesValue</td><td>Values of nodes before their activation </td></tr>
  </table>
  </dd>
</dl>

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<a class="anchor" id="ac065c297b0a6bb668504798903aaa1e0"></a>
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          <td class="memname">int RTRL::kronneckerDelta </td>
          <td>(</td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>i</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>k</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
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<p>The Kronnecker delta </p>
<dl class="section return"><dt>Returns</dt><dd>1 if i=0 , otherwise 0 </dd></dl>

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          <td class="memname">Matrix RTRL::predict </td>
          <td>(</td>
          <td class="paramname">)</td><td></td>
          <td></td>
        </tr>
      </table>
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<p>Predicts the future sensor values based on the current motor commands (inputs).</p>
<p>It consists of two steps.</p>
<ol type="1">
<li>Calculate activations (<dl class="section see"><dt>See Also</dt><dd><a class="el" href="classRTRL.html#ae5fe58a009095291e26cc03fbf38fd8c">simulateNet()</a> )</dd></dl>
</li>
<li>Update weights based on error (<dl class="section see"><dt>See Also</dt><dd><a class="el" href="classRTRL.html#a335e29295c02ad387560296392bc2c72">updateWeights()</a> ) </dd></dl>
<dl class="section return"><dt>Returns</dt><dd>Matrix that contains the predicted sensory values. </dd></dl>
</li>
</ol>

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          <td class="memname">void RTRL::setDesiredOut </td>
          <td>(</td>
          <td class="paramtype">Matrix&#160;</td>
          <td class="paramname"><em>fut_sensor</em>)</td><td></td>
          <td></td>
        </tr>
      </table>
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<p>Sets the desired output of the network. This method is called in controler's method : learn</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">fut_sensor</td><td>the robot's sensory values </td></tr>
  </table>
  </dd>
</dl>

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          <td class="memname">void RTRL::setState </td>
          <td>(</td>
          <td class="paramtype">Matrix&#160;</td>
          <td class="paramname"><em>motors</em>)</td><td></td>
          <td></td>
        </tr>
      </table>
</div><div class="memdoc">
<p>Set the current inputs of the network</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">motors</td><td>The values of robot's motors </td></tr>
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<p>Calculate the network's activations</p>
<dl class="section return"><dt>Returns</dt><dd>Matrix with the values of each node activation </dd></dl>

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<p>The squashing function of the network. the hyperbolic tangent used (tanh)</p>
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<p>The derivative of squashing function of the network</p>
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    <tr><td class="paramname">node</td><td>the value that will be mapped </td></tr>
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<p>Update weights based on gradient descent and sensitivity. </p>
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    <tr><td class="paramname">the</td><td>nodes activations </td></tr>
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<h2 class="groupheader">Member Data Documentation</h2>
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          <td class="memname">double RTRL::learningRate</td>
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<p>The learning rate of the algirith set 0.01 </p>

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<p>Vector which contains one matrix per output node, the sensitivity of each output node </p>

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<p>The values of nodes before activation of the network </p>

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<p>The number of input nodes </p>

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<p>The number of output nodes </p>

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<p>The number of reservoir nodes </p>

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<p>The values of the output nodes </p>

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<p>Matrix containing the weights ofthe network </p>

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<hr/>The documentation for this class was generated from the following files:<ul>
<li><a class="el" href="rtrl_8h_source.html">rtrl.h</a></li>
<li><a class="el" href="rtrl_8cpp.html">rtrl.cpp</a></li>
</ul>
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